Solution walkthrough

This section provides a detailed review of the visual anomaly detection example to interpret anomalies identified by a pre-trained VGG16 model. The sample notebook, chapter5_gradcam_cv.ipynb, can be found in the book’s GitHub repo:

1. First, let’s install the required packages using the requirements.txt file:

   import sys

   !{sys.executable} -m pip install -qr requirements.txt

2. Load the essential libraries:

   import cv2

   import os

   import re

   import glob

   import random

   import warnings

   import numpy as np

   import pandas as pd

   import scipy as sp

   import matplotlib.pyplot as plt

   import matplotlib.cm as cm

   import matplotlib.image as mpimg

   from platform import python_version

   from IPython.display import Image, display

   from sklearn.model_selection import train_test_split

   from sklearn.metrics import classification_report, accuracy_score, confusion_matrix, ConfusionMatrixDisplay

   import tensorflow as tf

   from tensorflow import keras

   from tensorflow.keras.models import *

   from tensorflow...